IJMLC 2014 Vol.4(1): 57-62 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.386

Skin Segmentation Using GMM Classifier and Texture Feature Extraction

Chi-Man Pun and Pan Ng

Abstract—In this paper, a skin color segmentation approach by texture feature extraction and k-mean clustering is proposed. We improved the traditional skin classification by combining both color and texture features for skin segmentation. After the color segmentation using a 16 – GMM (Gaussian Mixture Models) classifier, the texture features are extracted using effective wavelet transform with a 2-D Daubechies Wavelet and represented as a list of Shannon entropy. The non-skin regions can be eliminated by the Skin Texture-cluster Elimination using K-mean clustering. Experimental results based on common datasets show that our proposed can achieve better performance compared to the existing methods with true positive of 96.5% and with false positives 25.2% for the worst case, with true positive of 90.3% and with false positives 20.5% for the normal case.

Index Terms—Skin segmentation, texture feature, wavelet transform, k-mean clustering.

C.-M. Pun and P. Ng are with the Department of Computer and Information Science, University of Macau , Macau S.A.R., China (e-mail: cmpun@umac.mo).

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Cite:Chi-Man Pun and Pan Ng, "Skin Segmentation Using GMM Classifier and Texture Feature Extraction," International Journal of Machine Learning and Computing vol.4, no. 1, pp. 57-62, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net